Summary of Multimodal Latent Diffusion Model For Complex Sewing Pattern Generation, by Shengqi Liu et al.
Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation
by Shengqi Liu, Yuhao Cheng, Zhuo Chen, Xingyu Ren, Wenhan Zhu, Lincheng Li, Mengxiao Bi, Xiaokang Yang, Yichao Yan
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes SewingLDM, a multi-modal generative model that generates sewing patterns controlled by text prompts, body shapes, and garment sketches. The method extends the original vector of sewing patterns to cover more intricate details and compresses them into a compact latent space. A two-step training strategy is designed to inject multi-modal conditions into a diffusion model, ensuring generated garments are body-suited and detail-controlled. Comprehensive experiments show the effectiveness of SewingLDM, significantly surpassing previous approaches in terms of complex garment design and various body adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sewing patterns for making clothes are important because they can be changed easily on computers. Old methods could make nice clothes but struggled with complicated designs. The authors created a new model called SewingLDM that uses text prompts, body shapes, and sketches to create sewing patterns. This allows for more detailed control over the design and makes it easier to adapt the pattern to different body types. The results show that this method is better than previous ones at making complex garments. |
Keywords
» Artificial intelligence » Diffusion model » Generative model » Latent space » Multi modal
